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Found 22 papers, 5 papers with code
A Region-Shrinking-Based Acceleration for Classification-Based Derivative-Free Optimization
Derivative-free optimization algorithms play an important role in scientific and engineering design optimization problems, especially when derivative information is not accessible.
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
Classification of lung cancer subtypes on CT images with synthetic pathological priors
The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements.
HiTSKT: A Hierarchical Transformer Model for Session-Aware Knowledge Tracing
Knowledge tracing (KT) aims to leverage students' learning histories to estimate their mastery levels on a set of pre-defined skills, based on which the corresponding future performance can be accurately predicted.
Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables
Recently, discrete latent variable models have received a surge of interest in both Natural Language Processing (NLP) and Computer Vision (CV), attributed to their comparable performance to the continuous counterparts in representation learning, while being more interpretable in their predictions.
The HoloLens in Medicine: A systematic Review and Taxonomy
The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality display, is the main player in the recent boost in medical augmented reality research.
Neural Attention-Aware Hierarchical Topic Model
Neural topic models (NTMs) apply deep neural networks to topic modelling.
Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence
This paper proposes a transformer over transformer framework, called Transformer$^2$, to perform neural text segmentation.
Leveraging Information Bottleneck for Scientific Document Summarization
This paper presents an unsupervised extractive approach to summarize scientific long documents based on the Information Bottleneck principle.
Learning to Rearrange Voxels in Binary Segmentation Masks for Smooth Manifold Triangulation
However, downsampling can lead to a loss of image quality, which is undesirable especially in reconstruction tasks, where the fine geometric details need to be preserved.
AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo
The standard imaging modality for diagnosis and monitoring is computed tomography (CT), which can provide a detailed picture of the aorta and its branching vessels if completed with a contrast agent, called CT angiography (CTA).
Federated Learning Meets Natural Language Processing: A Survey
Recent Natural Language Processing techniques rely on deep learning and large pre-trained language models.
Topic Modelling Meets Deep Neural Networks: A Survey
Topic modelling has been a successful technique for text analysis for almost twenty years.
Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact
These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years.
Discriminative, Generative and Self-Supervised Approaches for Target-Agnostic Learning
For this task, we survey a wide range of techniques available for handling missing values, self-supervised training and pseudo-likelihood training, and adapt them to a suite of algorithms that are suitable for the task.
SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression
Obtaining training data for multi-document summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains.
Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention
It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting.
Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering
Based on the ELBOs, we propose a VAE-based Bayesian MF framework.
Variational Auto-encoder Based Bayesian Poisson Tensor Factorization for Sparse and Imbalanced Count Data
Under synthetic data evaluation, VAE-BPTF tended to recover the right number of latent factors and posterior parameter values.
A Technical Survey on Statistical Modelling and Design Methods for Crowdsourcing Quality Control
Our survey is the first to bridge the two branches by providing technical details on how they work together under frameworks that systematically unify crowdsourcing aspects modelled by both of them to determine the response quality.
Hierarchical clustering that takes advantage of both density-peak and density-connectivity
This paper focuses on density-based clustering, particularly the Density Peak (DP) algorithm and the one based on density-connectivity DBSCAN; and proposes a new method which takes advantage of the individual strengths of these two methods to yield a density-based hierarchical clustering algorithm.
Distinguishing Question Subjectivity from Difficulty for Improved Crowdsourcing
Experiments show that our model(1) improves the performance of both quality control for crowd-sourced answers and next answer prediction for crowd-workers, and (2) can potentially provide coherent rankings of questions in terms of their difficulty and subjectivity, so that task providers can refine their designs of the crowdsourcing tasks, e. g. by removing highly subjective questions or inappropriately difficult questions.
